def comp_crossshore_diff(data_list, title, cases, savename):
## Settings
sns.set_palette(sns.color_palette("muted"))
## Parameters
savename = savename.replace(".jpg", "") + "/cross_diff"
_yslices = np.array([7.56]) * 1e6
_tslice = 1.6e5
## Figure
fig, ax = plt.subplots(_yslices.size, 1)
fig.set_size_inches(FIGSIZE_NORMAL)
fig.set_dpi(FIG_DPI)
fig.set_tight_layout(True)
fig.suptitle(f"{title}\nChange in Cross-shore Profile")
if not isinstance(ax, np.ndarray):
ax = np.array([ax])
## Subplots
for i in range(ax.size):
_ax = ax[i]
_ax.axhline(color="black", linewidth=1)
_ax.axvline(color="black", linewidth=1)
_ax.grid()
_ax.set_xlim([0, 600])
_ax.set_ylim([-0.2, 0.2])
_ax.set_title(f"$y = {_yslices[i]/1000.}$km")
_ax.set_ylabel("$\\Delta SSE$ [m]")
if i == ax.size: _ax.set_xlabel("x [km]")
reference = data_list[0]["wl"].interp(t=fu.to_timestr(_tslice),
y=_yslices[i])
for _j in range(len(data_list) - 1):
j = _j + 1
data = data_list[j]
# Plot data
plt.plot(
data["x"] / 1000.,
data["wl"].interp(t=fu.to_timestr(_tslice), y=_yslices[i]) -
reference,
color=f"C{j}",
label=f"Case {cases[j]:02.0f}")
_ax.legend()
fig.savefig(savename, bbox_inches="tight", dpi=FIG_DPI)
print(f"Saved figure as '{savename}'")
return
def comp_alongshore_diff(data_list, title, cases, savename):
## Settings
sns.set_palette(sns.color_palette("muted"))
## Parameters
savename = savename.replace(".jpg", "") + "/along_diff"
_ylims = np.array([[0, 4], [1, 6], [2, 8], [3, 10]]) * 1e3
_times = [4e4, 8e4, 12e4, 16e4]
## Figure
fig, ax = plt.subplots(2, 2, sharex=False, sharey=True)
fig.set_size_inches(FIGSIZE_NORMAL)
fig.set_dpi(FIG_DPI)
fig.set_tight_layout(True)
fig.suptitle(f"{title}\nChange in Along-shore Profile")
# Subplots
for i in range(ax.size):
_ax = ax[i // 2, i % 2] # select subplot
_ax.grid()
_ax.set_xlim(_ylims[i])
_ax.set_ylim([-0.2, 0.2])
_ax.set_title(f"$t = {_times[i]:0.0f}$s")
_ax.axhline(color="black", linewidth=1)
_ax.axvline(color="black", linewidth=1)
if i // 2: _ax.set_xlabel("$y$ [km]")
if not i % 2: _ax.set_ylabel("$\\Delta SSE$ [m]")
reference = data_list[0]["wl"].interp(t=fu.to_timestr(_times[i]),
x=10e3)
for _j in range(len(data_list) - 1):
j = _j + 1
data = data_list[j]
_ax.plot(data["y"] / 1000.,
data["wl"].interp(t=fu.to_timestr(_times[i]), x=10e3) -
reference,
color=f"C{j}",
label=f"Case {cases[j]:02.0f}")
if i == 0:
_ax.legend()
fig.savefig(savename, bbox_inches="tight", dpi=FIG_DPI)
print(f"Saved figure as '{savename}'")
return
def comp_crossshore(data_list, title, cases, savename):
## Settings
sns.set_palette(sns.color_palette("muted"))
## Parameters
savename = savename.replace(".jpg", "") + "/cross"
_yslices = np.array([7.56]) * 1e6
_tslice = 1.6e5
## Figure
fig, ax = plt.subplots(_yslices.size, 1)
fig.set_size_inches(FIGSIZE_NORMAL)
fig.set_dpi(FIG_DPI)
fig.set_tight_layout(True)
fig.suptitle(f"{title}\nCross-shore Profile of Sea Surface Elevation")
if not isinstance(ax, np.ndarray):
ax = np.array([ax])
## Subplots
for i in range(ax.size):
_ax = ax[i]
_ax.axhline(color="black", linewidth=1)
_ax.axvline(color="black", linewidth=1)
_ax.grid()
_ax.set_xlim([0, 600])
_ax.set_ylim([0, 0.9])
_ax.set_title(f"$y = {_yslices[i]/1000.}$km")
_ax.set_ylabel("$SSE$ [m]")
if i == ax.size: _ax.set_xlabel("x [km]")
for j in range(len(data_list)):
data = data_list[j]
# Compute best fit parameters
k0, y0 = fa.compute_decay_parameter(data, _yslices[i], _tslice)
# Plot data
plt.plot(data["x"] / 1000.,
data["wl"].interp(t=fu.to_timestr(_tslice),
y=_yslices[i]),
color=f"C{j}",
label=f"Case {cases[j]:02.0f}")
# Plot best fit
plt.plot(data["x"] / 1000.,
fa.exp_decay(data["x"], k0, y0),
color=f"C{j}",
linestyle="--",
label=f"Best fit: $1/k_0 = {1./k0/1000.:0.1f}$km")
_ax.legend()
fig.savefig(savename, bbox_inches="tight", dpi=FIG_DPI)
print(f"Saved figure as '{savename}'")
return
def compute_decay_parameter(data, y, t):
""""Computes the decay parameter and scaling parameter for data at given y and t """
wl = data["wl"].interp(t=fu.to_timestr(t), y=y)
if np.all(np.isnan(wl)):
return np.nan, 0
popt, _ = curve_fit(
exp_decay, # f
data["x"], # xdata
wl, # ydata
p0=[1. / 100000., 1.], # p0
)
k0, y0 = popt
return k0, y0
def vis_contour(data,
t,
saveloc=None,
keep_open=False,
variable="wl",
xlims=None,
ylims=None):
## Check input
if variable in ["wl"]:
cmap = cmo.cm.balance
elif variable in ["u", "v"]:
cmap = cmo.cm.delta
else:
raise ValueError(f"Excpected {variable=} to be 'wl', 'u' or 'v'")
if np.isscalar(t):
t_arr = np.array([t])
elif isinstance(t, list):
t_arr = np.array(t)
elif isinstance(t, (list, np.ndarray)):
t_arr = t
else:
raise ValueError(f"{t=} is not an array, but {type(t)}")
t_num = t_arr.size
del t
## Paths
if saveloc is None:
saveloc = os.path.dirname(os.path.realpath(__file__)) + "/tests"
if saveloc.endswith(".jpg"):
saveloc.replace(".jpg", "")
os.makedirs(saveloc, exist_ok=True)
savename = saveloc + f"/contour_{variable}_n{t_num}_{np.mean(t_arr)/3600:0.0f}"
## Get parameters
x = data["x"]
y = data["y"]
var = data[variable]
x = x - x.min()
var_max = float(np.nanmax([np.abs([var.max(), var.min()])]))
var_min = -1. * var_max
if xlims is None:
xlims = [y.min() / 1000. / 10., y.max() / 1000.]
if ylims is None:
ylims = [0, 200]
## Figure
fig, ax = plt.subplots(t_num, 1, squeeze=False, sharex=True)
fig.set_size_inches(FIGSIZE_LONG)
fig.set_dpi(FIG_DPI)
# fig.set_tight_layout(True)
ax = np.ravel(ax)
norm = Normalize(var_min, var_max)
im = cm.ScalarMappable(norm=norm, cmap=cmap)
## Subplots
for i in range(t_num):
ax[i].contourf(y / 1000,
x / 1000,
var.interp(t=fu.to_timestr(t_arr[i])).T,
100,
cmap=cmap,
norm=norm,
vmin=var_min,
vmax=var_max)
ax[i].set_ylabel("$x$ [km]")
# ax[i].set_title(f"$t = {t_arr[i] / 3600:0.1f}$h")
ax[i].annotate(f"$t = {t_arr[i] / 3600:0.1f}$h",
xy=(0.99, 0.98),
xycoords="axes fraction",
ha="right",
va="top")
ax[i].set_xlim(xlims)
ax[i].set_ylim(ylims)
ax[-1].set_xlabel("$y$ [km]")
fig.colorbar(im,
ax=ax.ravel().tolist(),
label="Sea Surface Elevation [m]",
aspect=50)
## Save figure
fig.savefig(savename,
bbox_inches="tight",
dpi=FIG_DPI,
pil_kwargs={
"optimize": True,
"compress_level": 9
})
print(f"Saved figure {savename}")
## End
if not keep_open:
plt.close("all")
def vis_crossshore(data, y=1e5, t=3600, saveloc=None, keep_open=False):
## Check input
if np.isscalar(y):
y_list = np.array([y])
elif isinstance(y, (list, np.ndarray)):
y_list = np.array(y)
else:
raise ValueError(f"{y=} is not a number or array_like")
if np.isscalar(t):
t_list = np.array([t])
elif isinstance(t, (list, np.ndarray)):
t_list = np.array(t)
else:
raise ValueError(f"{t=} is not a number or array_like")
del t, y
y_num = y_list.size
t_num = t_list.size
if (t_num == 1) and (y_num == 1):
figsize = FIGSIZE_NORMAL
elif (t_num == 1) and (y_num > 1):
figsize = FIGSIZE_WIDE
elif (t_num > 1) and (y_num == 1):
figsize = FIGSIZE_HIGH
elif (t_num > 1) and (y_num > 1):
figsize = FIGSIZE_SQUARE
else:
raise ValueError(
f"Error in determining figsize for {y_num=} and {t_num=}")
## Paths
if saveloc is None:
saveloc = os.path.dirname(os.path.realpath(__file__)) + "/tests"
if saveloc.endswith(".jpg"):
saveloc.replace(".jpg", "")
os.makedirs(saveloc, exist_ok=True)
savename = saveloc + "/cross_shore_"
if y_num == 1:
savename += f"y{y_list[0]/1000:0.0f}_"
else:
savename += f"yn{y_num}_"
if t_num == 1:
savename += f"t{t_list[0]/3600:0.0f}"
else:
savename += f"tn{t_num}"
## Figure
fig, ax = plt.subplots(t_num,
y_num,
squeeze=False,
sharex=True,
sharey=True)
fig.set_size_inches(figsize)
fig.set_dpi(FIG_DPI)
fig.set_tight_layout(True)
for i in range(t_num):
for j in range(y_num):
# Make best fit
k0, y0 = fa.compute_decay_parameter(data, y_list[j], t_list[i])
wl_model = fa.exp_decay(data["x"], k0, y0)
# Plot data
ax[i, j].plot(data["x"] / 1000.,
data["wl"].interp(y=y_list[j],
t=fu.to_timestr(t_list[i])),
color="C0",
label="Waterlevel")
# Plot best fit
ax[i, j].plot(data["x"] / 1000.,
wl_model,
color="C0",
linestyle="--",
label=f"Best fit with $1/k0={1./k0/1000.:0.1f}$ km")
#
ax[i, j].axhline(color="black", linewidth=1)
ax[i, j].legend()
ax[i, j].set_xlim(0, data["x"].max() / 1000.)
ax[i, j].set_title(
f"$y={y_list[j]/1000:0.0f}$ km; $t={t_list[i]/3600:0.1f}$ hours"
)
ax[i, j].grid()
for i in range(t_num):
ax[i, 0].set_ylabel("$SSE$ [m]")
for j in range(y_num):
ax[-1, j].set_xlabel("$x$ [km]")
fig.savefig(savename,
bbox_inches="tight",
dpi=FIG_DPI,
pil_kwargs={
"optimize": True,
"compress_level": 9
})
print(f"Saved figure {savename}")
## End
if not keep_open:
plt.close("all")
def vis_alongshore(data, t=3600, x=1e4, saveloc=None, keep_open=False):
## Check input
if not np.isscalar(x):
raise ValueError(f"{x=} is not a number")
if np.isscalar(t):
t_list = np.array([t])
elif isinstance(t, (list, np.array)):
t_list = np.array(t)
else:
raise ValueError(f"{t=} is not a number or array_like")
t_num = t_list.size
del t
if t_num < 3:
figsize = FIGSIZE_NORMAL
else:
figsize = FIGSIZE_HIGH
## Paths
if saveloc is None:
saveloc = os.path.dirname(os.path.realpath(__file__)) + "/tests"
if saveloc.endswith(".jpg"):
saveloc.replace(".jpg", "")
os.makedirs(saveloc, exist_ok=True)
savename = saveloc + f"/along_shore_x{x/1000:0.0f}_tn{t_num}"
## Extract data
y = data["y"]
wl = data["wl"].interp(t=fu.to_timestr(t_list), x=x)
## Figure
fig, ax = plt.subplots(t_num, 1, squeeze=False, sharex=True)
fig.set_size_inches(figsize)
fig.set_dpi(FIG_DPI)
fig.set_tight_layout(True)
ax = np.ravel(ax)
for i in range(t_num):
ax[i].plot(y / 1000., wl[i])
ax[i].axhline(color="black", linewidth=1)
ax[i].set_xlim(0, y.max() / 1000.)
ax[i].set_ylabel("$SSE$ [m]")
ax[i].set_title(
f"$x={x/1000:0.0f}$ km and $t={t_list[i]/3600:0.1f}$ hours")
ax[i].grid()
ax[-1].set_xlabel("$y$ [km]")
fig.savefig(savename,
bbox_inches="tight",
dpi=FIG_DPI,
pil_kwargs={
"optimize": True,
"compress_level": 9
})
print(f"Saved figure {savename}")
## End
if not keep_open:
plt.close("all")
### Figure pressure
print("Figure: pressure")
plot_times = [4e4, 8e4, 12e4, 16e4]
try:
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
fig.set_size_inches(FIGSIZE_SQUARE)
fig.set_dpi(FIG_DPI)
fig.suptitle("Pressure distribution [Pa]")
fig.set_tight_layout(True)
for i in range(4):
_ax = ax[i // 2, i % 2]
im = _ax.contourf(x / 1000,
y / 1000,
p.interp(t=fu.to_timestr(plot_times[i])),
vmin=0,
vmax=2000)
_ax.set_xlim([0, 300])
_ax.set_ylim([0, y.max() / 1000])
_ax.set_title(f"$t = {plot_times[i] : 0.0f}$s")
if i // 2: _ax.set_xlabel("$x$ [km]")
if not i % 2: _ax.set_ylabel("$y$ [km]")
fig.colorbar(im, ax=_ax)
# _ax.axhline(7.56e3, linestyle="--", color="gray", linewidth=1)
# _ax.axvline(10, linestyle="--", color="gray", linewidth=1)
# _ax.axvline(100, linestyle="--", color="gray", linewidth=1)
# _ax.axvline(200, linestyle="--", color="gray", linewidth=1)